---
title: "Data Analysis Dashboard"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    vertical_layout: fill
    social: [ "twitter", "facebook", "menu"]
    source_code: embed
---


```{r setup, include=FALSE}
library(flexdashboard)
library(modelr)
library(broom)
library(caret)
library(rpart)
library(ggplot2)
library(Amelia)
library(dplyr)
library(data.table)
library(tidyverse)
```


```{r}
crypto_advertisement <- read.csv('~/R/R dashboards/advertising.csv')
```


Data Visualization
=====================================

## Row 1 {data-height=110}

### Number of countries

```{r}
#unique(crypto_advertisement$Country) 
valueBox(237, icon = "fa-ship", color="rgb(100,100,100)")
```

### Percentage of Clicks on Ad

```{r}
#table(crypto_advertisement$Clicked.on.Ad) 

valueBox("50 %", icon = "fa-heart", color="rgb(200,100,100)")
```

### Income Range for users 

```{r}
#max(crypto_advertisement$Area.Income)


valueBox("14000-79500", icon = "fa-life-ring",color="rgb(26,110,204)")
```
    

## Row 2 {data-height=400} 

### Age Distribution

```{r}
#install.packages('highcharter')
library(highcharter) 

hc <- hchart(
  density(crypto_advertisement$Age), 
  type = "area", name = "Users age"
  )
hc

#hist(crypto_advertisement$Age, breaks=12, col="cyan3",xlab="Ages of Users", main='Age distribution of users')
```


### Internet Usage 
```{r}
#hist(crypto_advertisement$Daily.Internet.Usage, breaks=12, col="cyan4",xlab="Daily Internet Usage", main='Daily Internet Usage distribution of users')

daily.internet.usage <- hchart(
  crypto_advertisement$Daily.Internet.Usage,breaks=12,
  color = "#008B8B", name = "Daily.Internet.Usage"
  )
daily.internet.usage
```

More Visualizations
=====================================================


## Row 1 

### Area Income compared to time spent on site
```{r}
#install.packages(plotly) # if you haven't installed the package
library(plotly)

m <- ggplot(data = crypto_advertisement, aes(x = Area.Income, y = Daily.Time.Spent.on.Site)) +
    geom_point() + # then add a layer of points
    geom_smooth(method = "lm") # and then add a fitted line

ggplotly(m) 

```


### Daily.Internet.Usage compared to time spent on site
```{r}
cv <- ggplot(data = crypto_advertisement, aes(x = Daily.Internet.Usage, y = Daily.Time.Spent.on.Site)) +
    geom_point() + # then add a layer of points
    geom_smooth(method = "lm") # and then add a fitted line

ggplotly(cv) 

```


Row
------------------------------------
### Gender Representations

```{r}
#changing column name
names(crypto_advertisement)[names(crypto_advertisement) == "Male"] <- "Gender"

#replacing gender values
crypto_advertisement <- within(crypto_advertisement, Gender[Gender == 0] <- 'Female')
crypto_advertisement <- within(crypto_advertisement, Gender[Gender == 1] <- 'Male')


pie(table(crypto_advertisement$Gender), main = "Gender Representations", 
    col = c("darkcyan", "darkgray"), radius = 1)
```

Report
========================================

Created by: Mercy

Dates :